JP2023049944A - Image processing device, method, and program - Google Patents

Abstract

To allow a bone density and a trabecula structure to be checked at a glance in an image processing device, a method, and a program.SOLUTION: A processor derives a bone part image of a subject from a first radiation image and a second radiation image acquired by imaging the subject including a bone part by radioactive rays with different energy distributions; derives a bone density image indicating bone density in a bone part region of the subject from the bone part image; derives a trabecula image indicating a trabecula structure from the first radiation image, the second radiation image, or the bone part image; and superimposes the trabecula image on the bone density image and displays it.SELECTED DRAWING: Figure 3

Description

The present disclosure relates to an image processing device, method and program.

Conventionally, there have been proposed methods of obtaining information about the strength of bones of a human body from radiographic images and displaying the information. For example, in Patent Document 1, a bone portion image representing the bone portion of a subject is obtained by energy subtraction processing, bone mineral information is obtained from the bone portion image, and an index value regarding the density of the bone mineral information and the trabecular structure of the bone is obtained. A method has been proposed in which the bone strength distribution is derived from the bone strength distribution, and the bone strength distribution is displayed in a color-coded manner. Further, in Patent Document 2, an image representing the distribution of bone mass, that is, bone density derived from a radiographic image is derived, and a trabecular bone image representing the structure of the trabecular bone corresponding to the bone density is acquired separately from the radiographic image, A technique for displaying a bone density distribution image and a trabecular bone image has been proposed.

JP 2019-202035 A JP 2021-069791 A

However, the method described in Patent Literature 1 can confirm the bone strength in the displayed image, but cannot confirm the structure of the trabecular bone. Further, in the method described in Patent Document 2, since the trabecular bone image is not derived from the radiographic image of the subject, it is necessary to separately perform imaging to acquire the trabecular image. Further, in the method described in Patent Document 2, since the bone density distribution image and the bone trabecular image are displayed separately, the bone density and the structure of the trabecular bone cannot be confirmed at a glance.

The present disclosure has been made in view of the circumstances described above, and aims to enable confirmation of bone density and trabecular bone structure at a glance.

An image processing apparatus according to the present disclosure includes at least one processor, and the processor acquires a first radiographic image and a second radiographic image obtained by imaging a subject including bones with radiation having different energy distributions, Deriving a bone image of the subject,
deriving a bone density image representing the bone density in the bone region of the subject from the bone image;
deriving a trabecular image representing the structure of the trabecular bone from the first radiographic image, the second radiographic image or the bone image;
The trabecular bone image is superimposed on the bone density image and displayed.

Note that in the image processing method according to the present disclosure, the processor may derive a high-frequency component of the first radiographic image, the second radiographic image, or the bone image as a trabecular bone image.

In this case, the processor may derive the enhanced high-frequency component as a trabecular bone image.

Further, in the image processing device according to the present disclosure, the processor derives the bone density of each pixel in the bone region, and derives a bone density image in which pixel values are low-frequency components of the bone density distribution. good too.

Further, in the image processing apparatus according to the present disclosure, the processor derives the bone density of each pixel in the bone region, and derives a bone density image in which the pixel value is the representative value of the bone density in the bone region. good too.

Further, in the image processing apparatus according to the present disclosure, the bone density image may have a color distribution according to bone density.

Also, in the image processing apparatus according to the present disclosure, the trabecular bone image may have a color different from the bone density.

An image processing method according to the present disclosure derives a bone image of a subject from a first radiographic image and a second radiographic image obtained by imaging a subject including a bone with radiation having different energy distributions,
deriving a bone density image representing the bone density in the bone region of the subject from the bone image;
deriving a trabecular image representing the structure of the trabecular bone from the first radiographic image, the second radiographic image or the bone image;
The trabecular bone image is superimposed on the bone density image and displayed.

Note that the image processing method according to the present disclosure may be provided as a program that causes a computer to execute.

According to the present disclosure, bone density and trabecular structure can be identified at a glance.

1 is a schematic block diagram showing the configuration of a radiation imaging system to which an image processing apparatus according to an embodiment of the present disclosure is applied; FIG. A diagram showing a schematic configuration of an image processing apparatus according to the present embodiment. A diagram showing the functional configuration of the image processing apparatus according to the present embodiment. Diagram showing soft tissue images Diagram showing the relationship of contrast between bone and soft tissue with respect to body thickness Diagram showing lookup table for obtaining correction factor Vertebral cross section Diagram showing trabecular images A diagram showing a mapping image of bone density Diagram showing the display screen Flowchart showing processing performed in the present embodiment

Embodiments of the present disclosure will be described below with reference to the drawings. FIG. 1 is a schematic block diagram showing the configuration of a radiographic imaging system to which an image processing apparatus according to an embodiment of the present disclosure is applied. As shown in FIG. 1, the radiation imaging system according to this embodiment includes an imaging device 1 and an image processing device 10 according to this embodiment.

The imaging apparatus 1 irradiates a first radiation detector 5 and a second radiation detector 6 with radiation such as X-rays emitted from a radiation source 3 and transmitted through a subject H with different energies. This is an imaging device for performing energy subtraction by the shot method. During imaging, as shown in FIG. 1, a first radiation detector 5, a radiation energy conversion filter 7 made of a copper plate or the like, and a second radiation detector 6 are arranged in this order from the radiation source 3 side. to drive the radiation source 3 . The first and second radiation detectors 5 and 6 and the radiation energy conversion filter 7 are in close contact.

As a result, the first radiation detector 5 acquires a first radiographic image G1 of the subject H using low-energy radiation including so-called soft rays. Also, the second radiation detector 6 acquires a second radiographic image G2 of the subject H by high-energy radiation from which soft rays have been removed. The first and second radiographic images are input to the image processing device 10 .

The first and second radiation detectors 5 and 6 are capable of repeatedly recording and reading radiographic images, and are so-called direct radiation detectors that directly receive radiation to generate charges. may be used, or a so-called indirect type radiation detector that converts radiation to visible light and then converts the visible light to charge signals may be used. As for the readout method of the radiographic image signal, the so-called TFT readout method, in which the radiographic image signal is read out by turning on/off a TFT (thin film transistor) switch, or the radiographic image signal is read out by irradiating the readout light. Although it is desirable to use a so-called optical readout system in which .

Next, an image processing apparatus according to this embodiment will be described. First, the hardware configuration of the image processing apparatus according to this embodiment will be described with reference to FIG. As shown in FIG. 2, the image processing apparatus 10 is a computer such as a workstation, server computer, or personal computer, and includes a CPU (Central Processing Unit) 11, a nonvolatile storage 13, and a memory 16 as a temporary storage area. Prepare. The image processing apparatus 10 also includes a display 14 such as a liquid crystal display, an input device 15 such as a keyboard and a mouse, and a network I/F (Interface) 17 connected to a network (not shown). CPU 11 , storage 13 , display 14 , input device 15 , memory 16 and network I/F 17 are connected to bus 18 . Note that the CPU 11 is an example of a processor in the present disclosure.

The storage 13 is implemented by a HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, or the like. The image processing program 12 installed in the image processing apparatus 10 is stored in the storage 13 as a storage medium. The CPU 11 reads out the image processing program 12 from the storage 13 , expands it in the memory 16 , and executes the expanded image processing program 12 .

Note that the image processing program 12 is stored in a storage device of a server computer connected to a network or in a network storage in an externally accessible state, and is downloaded to a computer constituting the image processing device 10 in response to a request. Installed. Alternatively, it is recorded on a recording medium such as a DVD (Digital Versatile Disc), a CD-ROM (Compact Disc Read Only Memory), or the like, distributed, and installed in a computer constituting the image processing apparatus 10 from the recording medium.

Next, the functional configuration of the image processing apparatus according to this embodiment will be described. FIG. 3 is a diagram showing the functional configuration of the image processing apparatus according to this embodiment. As shown in FIG. 3 , the image processing apparatus 10 includes an image acquisition section 21 , a bone image generation section 22 , a first derivation section 23 , a second derivation section 24 and a display control section 25 . By executing the image processing program 12 , the CPU 11 functions as an image acquisition section 21 , a bone image generation section 22 , a first derivation section 23 , a second derivation section 24 and a display control section 25 .

The image acquiring unit 21 causes the imaging device 1 to perform energy subtraction imaging of the subject H, thereby obtaining a first radiation image G1 of the subject H and a second radiation image G1 of the subject H from the first and second radiation detectors 5 and 6. Acquire image G2. When acquiring the first radiographic image G1 and the second radiographic image G2, the imaging dose, radiation quality, tube voltage, distance between the radiation source 3 and the surfaces of the first and second radiation detectors 5 and 6 Imaging conditions such as SID (Source Image receptor Distance), SOD (Source Object Distance), which is the distance between the radiation source 3 and the surface of the object H, and the presence or absence of an anti-scatter grid are set.

The SOD and SID are used for calculating the body thickness distribution as described later. The SOD is preferably obtained with, for example, a TOF (Time Of Flight) camera. The SID is preferably obtained by, for example, a potentiometer, an ultrasonic rangefinder, a laser rangefinder, or the like.

The imaging conditions may be set by input from the input device 15 by the operator.

The bone image generator 22 generates a bone image Gb representing the bone region of the subject H from the first radiographic image G1 and the second radiographic image G2 acquired by the image acquisition unit 21 . FIG. 4 shows an example of the bone image Gb generated by the bone image generator 22. As shown in FIG. Lumbar vertebrae and femurs are generally used to measure bone mass or bone mineral content. Therefore, the bone image Gb shown in FIG. 4 is generated from the first radiographic image G1 and the second radiographic image G2 obtained by performing imaging so as to include part of the femur and lumbar spine of the subject H. A bone image Gb is shown.

The bone image generating unit 22 performs weighted subtraction between corresponding pixels on the first radiographic image G1 and the second radiographic image G2, as shown in the following equation (1), so that the first radiation A bone portion image Gb is generated in which only the bone portion of the subject H included in the image G1 and the second radiographic image G2 is extracted. Note that μb in the following equation (1) is a weighting coefficient, and x and y are the coordinates of each pixel of the bone image Gb.

Gb(x,y)=G1(x,y)-μb×G2(x,y) (1)

Here, each of the first radiographic image G1 and the second radiographic image G2 includes, in addition to the primary ray component of the radiation transmitted through the subject H, the scattered ray component based on the radiation scattered within the subject H. . Therefore, it is preferable to remove scattered radiation components from the first radiographic image G1 and the second radiographic image G2. The method of removing the scattered radiation component is not particularly limited, but for example, the method described in JP 2015-043959 is applied to remove the scattered radiation component from the first radiographic image G1 and the second radiographic image G2. You may When using the method described in Japanese Patent Application Laid-Open No. 2015-043959, the derivation of the body thickness distribution of the subject H and the derivation of the scattered radiation component for removing the scattered radiation component are performed at the same time.

The removal of the scattered radiation component from the first radiographic image G1 will be described below, but the removal of the scattered radiation component from the second radiographic image G2 can also be performed in the same manner. First, the bone image generator 22 acquires a virtual model of the subject H having an initial body thickness distribution T0(x, y). The virtual model is data that virtually represents the subject H, in which the body thickness according to the initial body thickness distribution T0(x, y) is associated with the coordinate position of each pixel of the first radiographic image G1. A virtual model of the subject H having the initial body thickness distribution T0(x, y) may be stored in the storage 13 of the image processing device 10 in advance. Further, the bone image generator 22 may calculate the body thickness distribution T(x, y) of the subject H based on the SID and SOD included in the imaging conditions. In this case, the initial body thickness distribution T0(x, y) can be obtained by subtracting SOD from SID.

Next, based on the virtual model, the bone image generating unit 22 generates an estimated primary line image obtained by estimating the primary line image obtained by imaging the virtual model, and an estimated primary line image obtained by estimating the scattered radiation image obtained by imaging the virtual model. An image synthesized with the scattered radiation image is generated as an estimated image obtained by estimating the first radiographic image G1 obtained by imaging the subject H. FIG.

Next, the bone image generator 22 corrects the initial body thickness distribution T0(x, y) of the virtual model so that the difference between the estimated image and the first radiographic image G1 is reduced. The bone image generator 22 repeats the generation of the estimated image and the correction of the body thickness distribution until the difference between the estimated image and the first radiographic image G1 satisfies a predetermined end condition. The bone image generation unit 22 derives the body thickness distribution when the end condition is satisfied as the body thickness distribution T(x, y) of the subject H. FIG. Further, the bone image generator 22 removes the scattered radiation component included in the first radiographic image G1 by subtracting the scattered radiation component when the end condition is satisfied from the first radiographic image G1.

The first derivation unit 23 derives a bone density image representing bone density in the bone region of the subject H based on the bone image Gb generated by the bone image generation unit 22 . The first derivation unit 23 derives the bone density image B by deriving the bone density B(x, y) for each pixel (x, y) of the bone image Gb. The bone density B(x, y) may be derived for all bones included in the bone image Gb, or the bone density B(x, y) may be derived only for a predetermined bone. . As an example, in the present embodiment, for the bone image Gb, a predetermined bone is the lumbar spine included in the bone image Gb, and the first derivation unit 23 calculates the bone density B for each pixel of the bone region corresponding to the lumbar spine. (x, y) is derived, and the bone density B(x, y) is not derived for bone regions corresponding to other bones included in the bone region image Gb. Hereinafter, the bone region from which the first derivation unit 23 derives the bone density B(x, y) refers to the bone region corresponding to the lumbar vertebrae.

Specifically, the first derivation unit 23 converts each pixel value Gb(x, y) of the bone region of the bone image Gb into a pixel value of the bone image acquired under the reference imaging condition, thereby obtaining each pixel value of the bone image Gb. A bone density B(x,y) corresponding to the pixel is derived. More specifically, the first derivation unit 23 corrects each pixel value Gb(x, y) of the bone image Gb using a correction coefficient obtained from a lookup table (not shown) to be described later. Bone density B(x, y) for each pixel is derived by

Here, the higher the tube voltage in the radiation source 3 and the higher the energy of the radiation emitted from the radiation source 3, the smaller the contrast between the soft tissue and the bone tissue in the radiographic image. In addition, in the course of the radiation passing through the subject H, the subject H absorbs the low-energy component of the radiation, causing beam hardening in which the energy of the radiation increases. The higher the energy of the radiation due to the beam hardening, the greater the body thickness of the subject H.

FIG. 5 is a diagram showing the relationship between the body thickness of the subject H and the contrast between the bone portion and the soft portion. Note that FIG. 5 shows the relationship between the body thickness of the subject H and the contrast between the bone and soft parts at three tube voltages of 80 kV, 90 kV and 100 kV. As shown in FIG. 5, the higher the tube voltage, the lower the contrast. Further, when the body thickness of the subject H exceeds a certain value, the greater the body thickness, the lower the contrast. The larger the pixel value Gb(x, y) of the bone region in the bone image Gb, the greater the contrast between the bone and soft tissue. Therefore, the relationship shown in FIG. 5 shifts to the high contrast side as the pixel value Gb(x, y) of the bone region in the bone image Gb increases.

In the present embodiment, a lookup table (see omitted) is stored in the storage 13 . The correction coefficient is a coefficient for correcting each pixel value Gb(x, y) of the bone image Gb.

FIG. 6 is a diagram showing an example of a lookup table stored in the storage 13. As shown in FIG. FIG. 6 exemplifies a lookup table LUT1 in which the reference imaging condition is set to a tube voltage of 90 kV. As shown in FIG. 6, in the lookup table LUT1, a larger correction coefficient is set as the tube voltage increases and the body thickness of the subject increases. In the example shown in FIG. 6, since the standard imaging condition is a tube voltage of 90 kV, the correction coefficient is 1 when the tube voltage is 90 kV and the body thickness is 0. Although the lookup table LUT1 is shown two-dimensionally in FIG. 6, the correction coefficients differ according to the pixel values of the bone region. Therefore, the lookup table LUT1 is actually a three-dimensional table to which an axis representing pixel values of the bone region is added.

The first derivation unit 23 calculates the correction coefficient C0(x, y) for each pixel according to the imaging conditions including the body thickness distribution T(x, y) of the subject H and the setting value of the tube voltage stored in the storage 13. , from the lookup table LUT1. Then, the first derivation unit 23 calculates the correction coefficient C0(x, y) for each pixel value Gb(x, y) of the bone region in the bone image Gb, as shown in the following equation (2). By multiplying by , the bone density B(x, y) for each pixel of the bone image Gb is derived. Thereby, a bone density image B having a pixel value of bone density B(x, y) is derived. The bone density B (x, y) derived in this way is obtained by imaging the subject H with a tube voltage of 90 kV, which is the standard imaging condition, and is a radiographic image from which the influence of beam hardening has been removed. It represents the pixel value of the bone portion of the included bone portion region.
B(x,y)=C0(x,y)×Gb(x,y) (2)

Note that the first derivation unit 23 derives the low-frequency component of the distribution of the bone density B(x, y) of each pixel of the bone image Gb, and calculates the bone density image so that the derived low-frequency component is used as the pixel value. B may be derived. Alternatively, a representative value of the bone density B(x, y) of each pixel of the bone image Gb may be derived, and the bone density image B may be derived such that the derived representative value is used as the pixel value. An average value, median value, maximum value, or minimum value can be used as the representative value. Thereby, when the bone density image B is displayed, it is possible to confirm the global change in the bone density.

The second derivation unit 24 derives a trabecular bone image K representing the structure of trabecular bone from the first radiographic image G1, the second radiographic image G2, or the bone image Gb. In this embodiment, the trabecular bone image K is derived from the bone image Gb. FIG. 7 is a cross-sectional view of a vertebra, which is an example of the cross-sectional structure of human bones. As shown in FIG. 7, human bones are composed of cancellous bone 31 and cortical bone 32 covering the outside of cancellous bone 31 . Cortical bone 32 is harder and denser than cancellous bone 31 . Trabecular bone 31 extends into the medullary cavity and is called trabecular bone. Trabeculae are aggregates of small bone pillars. Since the trabecular bone is composed of a lattice of bone plates and columns, the structure of the trabecular bone appears as a high-frequency component in the image. Therefore, the second derivation unit 24 derives the high-frequency component of the image of the bone region in the bone image Gb as the trabecular bone image K. FIG. Here, the pixel value K(x, y) of the trabecular bone image K is the value of the high frequency component of the image of the bone region in the bone image Gb. FIG. 8 shows the derived trabecular images. FIG. 8 shows the derived trabecular bone image K at high density. By using band information of about 0.1 to 0.2 cycle/mm or higher in the bone image Gb as high-frequency components, a trabecular bone image K including bone trabecular and cortical bone component information can be derived. Further, by using band information of about 0.4 to 1.0 cycle/mm or more in the bone image Gb as high frequency components, a trabecular bone image K including only thin lines of trabecular bone can be derived. On the other hand, the low-frequency component in the case of deriving the low-frequency component of the distribution of the bone density B(x, y) as the bone density image B is the frequency component in the band other than the band of the high-frequency component.

Note that the high-frequency components of the bone region image in the bone image Gb have low contrast, and may be difficult to visually recognize. Therefore, the second derivation unit 24 may emphasize high frequency components and derive the emphasized high frequency components as the trabecular bone image K. FIG.

The display control unit 25 superimposes the trabecular bone image K on the bone density image B and displays it on the display 14 . Specifically, the display control unit 25 superimposes the trabecular bone image K on the bone density image B to generate the composite image GC0, and displays the generated composite image GC0 on the display . When generating the composite image GC0, the display control unit 25 maps different colors to the bone density image B according to the values of the bone density B(x, y) to generate a mapping image Bm. FIG. 9 is a diagram showing a mapping image. In FIG. 9, different colors are indicated by different densities, and the higher the density, the higher the bone density. As a result, the mapping image Bm has a color distribution in the bone density image B corresponding to the bone density. In addition, in the mapping image Bm, instead of color mapping, density change according to bone density may be expressed.

Then, the display control unit 25 derives a composite image GC0 by superimposing the trabecular bone image K on the mapping image Bm. FIG. 10 is a diagram showing a display screen of a synthesized image. In addition, on the display screen 40 shown in FIG. 10, only the vertebral region of the bone image Gb shown in FIG. 4 is enlarged and displayed. In FIG. 10, the color of the trabecular bone image K is preferably different from the color of the mapping image Bm. For example, if the color of the mapping image Bm is a cold color, it is preferable that the color of the trabecular image K is a warm color.

More specifically, in the mapping image Bm, the minimum bone density is green ((R, G, B) = (0, 255, 0)), the maximum bone density is blue (0, 0, 255), and the bone density When B(x, y) is represented by N-step color changes, the pixel values of the mapping image Bm are set to (0, 255-255×(n−1)/N, 255×(n−1)/N ) (n=1, 2, . . . N). n indicates the range to which the bone density B(x, y) belongs when the range from the minimum value to the maximum value that the bone density B(x, y) can take is divided into N. For example, when the bone density B (x, y) takes a value of 0 to 255 and N = 16, if the bone density B (x, y) of a certain pixel is 10, n = 1, even if it is 120 For example, if n=8, 250 then n=16. On the other hand, in the trabecular image K, the minimum pixel value is yellow (255, 255, 0), the maximum pixel value is red (255, 0, 0), and the pixel value K (x, y) of the trabecular image K is M In the case of expressing by a stepwise color change, the pixel value of the trabecular image K is set to (255, 255-255×(m−1)/M, 255×(m−1)/M) (m=1, 2 , . . . M). In this case, making the color of the trabecular image K different from the color of the mapping image Bm means that the color of the mapping image Bm and the color of the trabecular image K do not match for any of the above n and m. Say things.

Note that the color change of the mapping image Bm and the trabecular image K does not have to be linear. For example, the pixel values of the mapping image Bm are derived by (0, 255-255×(n−1) ² /N ² , 255×(n−1) ² /N ² ), and the pixel values of the trabecular bone image K are (255, 255-255 x (m-1) ² /M ² , 255 x (m-1) ² /M ² ).

Next, processing performed in this embodiment will be described. FIG. 11 is a flow chart showing the processing performed in this embodiment. First, the image acquiring unit 21 acquires the first and second radiation images G1 and G2 having different energy distributions by causing the imaging device 1 to perform imaging (step ST1). Next, the bone image generator 22 derives a bone image Gb representing the bone region of the subject H from the first radiographic image G1 and the second radiographic image G2 acquired by the image acquisition unit 21 (step ST2).

Subsequently, the first derivation unit 23 derives a bone density image B representing bone density in the bone region of the subject H based on the bone image Gb generated by the bone image generation unit 22 (step ST3). Further, the second derivation unit 24 derives a trabecular bone image K representing the structure of the trabecular bone from the bone image Gb (step ST4). Then, the trabecular bone image K is superimposed on the bone density image B to derive a synthesized image GC0, the derived synthesized image GC0 is displayed on the display 14 (step ST5), and the process is terminated.

As described above, in the present embodiment, the trabecular bone image K is superimposed on the bone density image B, so that the bone density and the structure of the trabecular bone can be confirmed at a glance.

By using different colors for the bone density image B and the trabecular bone image K, the two can be easily distinguished and confirmed.

In the above embodiment, the first and second radiographic images G1 and G2 are acquired by the one-shot method when performing the energy subtraction processing, but the method is not limited to this. The first and second radiographic images G1 and G2 may be acquired by a so-called two-shot method, in which imaging is performed twice using only one radiation detector. In the case of the two-shot method, body motion of the subject H may shift the position of the subject H included in the first radiographic image G1 and the second radiographic image G2. Therefore, it is preferable to perform the processing of the present embodiment after aligning the subject in the first radiographic image G1 and the second radiographic image G2.

Further, in the above embodiment, the visceral fat mass distribution is derived using the first and second radiographic images acquired in the system for imaging the subject H using the first and second radiation detectors 5 and 6. However, the visceral fat mass distribution may be derived using first and second radiographic images G1 and G2 obtained using a stimulable phosphor sheet instead of the radiation detector. In this case, two stimulable phosphor sheets are superimposed and irradiated with radiation that has passed through the subject H, and the radiographic image information of the subject H is accumulated and recorded on each stimulable phosphor sheet. The first and second radiographic images G1 and G2 may be obtained by photoelectrically reading the radiographic image information. The two-shot method may also be used when the first and second radiographic images G1 and G2 are acquired using the stimulable phosphor sheet.

Moreover, the radiation in the above embodiment is not particularly limited, and α-rays, γ-rays, or the like can be used in addition to X-rays.

Further, in the above-described embodiment, a processing unit (processing unit) that executes various processes such as the image acquisition unit 21, the bone image generation unit 22, the first derivation unit 23, the second derivation unit 24, and the display control unit 25 As the hardware structure of , the following various processors can be used. In addition to the CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as described above, the various processors described above include circuits such as FPGAs (Field Programmable Gate Arrays) after manufacturing. Programmable Logic Device (PLD), which is a processor whose configuration can be changed, ASIC (Application Specific Integrated Circuit), etc. Circuits, etc. are included.

One processing unit may be configured with one of these various processors, or a combination of two or more processors of the same or different type (for example, a combination of multiple FPGAs or a combination of a CPU and an FPGA). ). Also, a plurality of processing units may be configured by one processor.

As an example of configuring a plurality of processing units in one processor, first, as represented by computers such as clients and servers, one processor is configured by combining one or more CPUs and software, There is a form in which this processor functions as a plurality of processing units. Secondly, as typified by System On Chip (SoC), etc., there is a form of using a processor that realizes the functions of the entire system including multiple processing units with a single IC (Integrated Circuit) chip. be. In this way, the various processing units are configured using one or more of the above various processors as a hardware structure.

Furthermore, more specifically, as the hardware structure of these various processors, an electric circuit (circuitry) in which circuit elements such as semiconductor elements are combined can be used.

REFERENCE SIGNS LIST 1 imaging device 3 radiation source 5, 6 radiation detector 7 radiation energy conversion filter 10 image processing device 11 CPU
12 image processing program 13 storage 14 display 15 input device 16 memory 17 network I/F
18 bus 21 image acquisition unit 22 bone image generation unit 23 first derivation unit 24 second derivation unit 25 display control unit 31 cancellous bone 32 cortical bone 40 display screen B bone density image Bm mapping image G1 first radiographic image G2 second Radiation image 2 Gb Bone image GC0 Composite image H Subject K Trabecular image

Claims (9)

comprising at least one processor;
The processor
Deriving a bone image of the subject from a first radiographic image and a second radiographic image obtained by imaging the subject including the bone with radiation having different energy distributions,
deriving a bone density image representing bone density in the bone region of the subject from the bone image;
deriving a trabecular bone image representing the structure of trabecular bone from the first radiographic image, the second radiographic image or the bone image;
An image processing apparatus for superimposing and displaying the trabecular bone image on the bone density image. The image processing apparatus according to claim 1, wherein the processor derives high-frequency components of the first radiographic image, the second radiographic image, or the bone image as the trabecular bone image. 3. The image processing apparatus according to claim 2, wherein the processor derives the enhanced high-frequency component as the trabecular bone image. 4. The processor according to any one of claims 1 to 3, wherein the processor derives the bone density of each pixel in the bone region, and derives the bone density image in which pixel values are low-frequency components of the bone density distribution. The described image processing device. 4. Any one of claims 1 to 3, wherein the processor derives the bone density of each pixel in the bone region, and derives the bone density image having a pixel value representing the representative value of the bone density in the bone region. 10. The image processing device according to claim 1. The image processing apparatus according to any one of claims 1 to 5, wherein the bone density image has a color distribution according to the bone density. 7. The image processing apparatus according to claim 6, wherein said bone trabecular image has a color different from said bone density. Deriving a bone image of the subject from a first radiographic image and a second radiographic image obtained by imaging the subject including the bone with radiation having different energy distributions,
deriving a bone density image representing bone density in the bone region of the subject from the bone image;
deriving a trabecular bone image representing the structure of trabecular bone from the first radiographic image, the second radiographic image or the bone image;
An image processing method for superimposing and displaying the trabecular bone image on the bone density image. A procedure for deriving a bone image of a subject from first and second radiographic images acquired by imaging a subject including a bone with radiation having different energy distributions;
a procedure of deriving a bone density image representing bone density in a bone region of the subject from the bone image;
a procedure of deriving a trabecular bone image representing a structure of trabecular bone from the first radiographic image, the second radiographic image, or the bone image;
and an image processing program for causing a computer to execute a procedure for superimposing and displaying the trabecular bone image on the bone density image.

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